The present invention relates generally to medical diagnostics, and more particularly to the determination of vessel boundaries in a medical image.
To diagnose a problem of a patient, medical professionals often have to examine the patient's vessels (e.g., blood vessels). To illuminate a vessel so that the medical professional can examine the vessel, a patient consumes (e.g., drinks) a contrast-enhancing agent. The contrast-enhancing agent brightens one or more vessels relative to the surrounding area.
The main goal of the majority of contrast-enhanced (CE) magnetic resonance angiography (MRA) and computed tomography angiography (CTA) is diagnosis and qualitative or quantitative assessment of pathology in the circulatory system. Once the location of the pathology is determined, quantitative measurements can be made on the original 2 dimensional slice data or, more commonly, on 2 dimensional multi planar reformat (MPR) images produced at user-selected positions and orientations. In the quantification of stenosis, it is often desirable to produce a cross-sectional area/radius profile of a vessel so that one can compare pathological regions to healthy regions of the same vessel.
Accurate and robust detection of vessel boundaries is traditionally a challenging task. In particular, a vessel boundary detection algorithm has to be accurate and robust so that the algorithm can be used to accurately detect vessel boundaries on many types of medical images. If the vessel boundary detection algorithm is inaccurate (even in a small number of cases), a medical professional (e.g., a radiologist) relying on the computer's output may, in turn, incorrectly diagnose the patient.
There are many reasons why accurate and robust detection of vessel boundaries is a challenging task. First, the presence of significant noise levels in computed tomography (CT) and magnetic resonance (MR) images often forms strong edges (i.e., changes in intensity between data points) inside vessels. Second, the size of a vessel can vary from one vessel location to another, resulting in additional edges. Third, the intensity profile of a vessel boundary can be diffused at one side while shallow on the other sides (e.g., due to the presence of other vessels or high contrast structures). Fourth, the presence of vascular pathologies, e.g., calcified plaques, often makes the shape of a vessel cross-sectional boundary locally deviate from a circular shape. These all result in additional edges that can affect an accurate determination of a vessel boundary.
FIG. 1A shows a three dimensional view of a vessel 104 having different contrasts along the vessel 104. Specifically, the top portion 108 of the vessel 104 is brighter than the bottom portion 112 of the vessel 104 because of the contrast agent taken by the patient. This change in contrast results in edges generated when an orthogonal (i.e., cross-sectional) view of the vessel 104 is used. These edges can result in inaccuracy when an algorithm is used to determine the boundaries of the vessel 104.
FIG. 1B shows an orthogonal view of three vessels 116, 120, 124. When the three vessels 116, 120, 124 are close together, one vessel's boundary is often difficult to distinguish from its neighboring vessel's boundary. FIG. 1C shows an orthogonal view of two vessels 128, 132. Each vessel's boundary is difficult to distinguish from the other's boundary because of the significant diffusion 134 of the boundaries.
There have been a variety of techniques that have been used to address the above mentioned challenges. For example, medical professional have estimated the boundary of a vessel using computer-aided drawing programs. This is an inaccurate process because the estimation of the boundary can vary widely from the actual boundary.
Another example is a “snake” model for segmenting vessel boundaries in the planes orthogonal to the vessel centerline. The “snake” model traditionally “inserts” a tube having a smaller diameter than the vessel into a representation of the vessel and then uses parameters to cause the tube to expand until reaching the vessel's walls. The selection of the parameters, however, are often initially estimated. An inaccurate selection of one or more parameters may result in the tube expanding beyond the actual vessel boundary. Thus, the snake model does not always provide accurate results.
Another attempt to address the above mentioned challenges is a ray propagation method. This method is based on the intensity gradients for the segmentation of vessels and detection of their centerline. However, the use of gradient strength by itself is often not enough for robust segmentation.
Another approach to solve the above-mentioned problem is based on explicit front propagation via normal vectors, which then combines smoothness constraints with mean-shift filtering. Specifically, the curve evolution equation ∂C(s,t)/∂t=S(x,y){right arrow over (N)} was determined for the vessel boundaries where C(s,t) is a contour, S(x,y) is the speed of evolving contour and {right arrow over (N)} is the vector normal to C(s,t). In this approach, the contour C(s,t) is sampled and the evolution of each sample is followed in time by rewriting the curve evolution equation in vector form. The speed of rays, S(x,y) depends on the image information and shape priors. S(x,y)=So(x,y)+βS1(x,y) was proposed where So(x,y) measures image discontinuities, S1(x,y) represents shape priors, and β balances these two terms. Image discontinuities are detected via mean-shift analysis along the rays. Mean-shift analysis, which operates in the joint spatial-range domain where the space of the 2 dimensional lattice represents the spatial domain and the space of intensity values constitutes the range domain, is often used for robustly detecting object boundaries in images. This approach is often effective when vessel boundaries are well isolated. It is often difficult, however, to estimate parameters such as spatial, range kernel filter sizes, and/or the amount of smoothness constraints for the robust segmentation of vessels. In particular, the use of a single spatial scale and curvature based smoothness constraints are typically not enough for accurate results when vessels are not isolated very well.
Therefore, there remains a need to more accurately and robustly detect vessel boundaries.